Both rigor and speed are necessary in data science processes in order to derive full value from all initiatives.
Accurately determining the ROI on new data science initiatives before they begin can prevent catastrophic losses in revenue, growth, and time.
The hallmark of a mature data science organization is that it can consistently, sustainably, and efficiently derive value from its data.
All else being equal, you want both rigor and speed. The business always needs speed. They need everything yesterday because time is money. Since the clock is always ticking, if you have the right processes in place, then it becomes very easy to work quickly. So it it's a little bit like when you're learning to code. Initially, it's really hard because you have all these rules to follow and you have to think differently, but once you develop a habit of ensuring that your code is well tested and your analyses or models are thoroughly reviewed, then everything becomes simpler.
It’s necessary to calculate the ROI on investing in data science initiatives to determine if they are really worth that investment. When calculating the cost of even something as simple as improvement on an existing initiative, you may discover that the initiative’s outcome doesn't actually offset the investment that's required, or it may offset it over a much longer period than what you expected. For example, let’s say you have a model with 80% accuracy and stakeholders are asking to increase the accuracy to 85% because it will increase sales. Initially, that may sound like a great idea, but depending on the model’s complexity, that 5% improvement might actually take an entire data team six months to accomplish. So, knowing this kind of information is vital in allocating resources wisely and effectively.